System and Method for Evaluating Search Queries to Identify Titles for Content Production

ABSTRACT

Systems and methods are provided to select potential titles for online content using search query logs from web search service providers. A plurality of search queries are collected from one or more web search service providers. A lifetime value is determined for each of the search queries. Potential titles are then selected from the plurality of search queries using selection criteria including the lifetime value of the search queries. The potential titles can then be provided to content developers who develop online content based on the potential titles.

FIELD OF THE TECHNOLOGY

At least some embodiments disclosed herein relate, in general, tosystems for analyzing the content of web search queries, and moreparticularly, to identifying titles for online content that may be ofinterest to end users.

BACKGROUND

There is a wide variety of content available on the Internet, but suchcontent may fail to provide full coverage of many topics of interest tousers. This information gap provides an opportunity for informationservice providers to create new content or repackage existing onlinecontent that relates to such topics. Creation of such content createsrevenue generation opportunities. Service providers can be enabled to,for example, derive direct revenue from selling such content to otherwebsite providers or directly to users, or via advertising revenuesassociated with website providing such content to users for free.

One of the greatest challenges, however, is identifying content that hasthe greatest potential for generating revenue. Within a narrow subjectarea, experts in the subject area may have a rough idea of what topicsare of the greatest interest to users, but cannot provide quantitativeestimates of potential revenue streams from content created relating tosuch topics. Furthermore, in some cases, popular interests may run aheadof expert's knowledge, or there may be no subject matter experts at allrelating to such topics.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which like referencesindicate similar elements.

FIG. 1 illustrates a high-level view of components of one embodiment ofsystems that support evaluating search queries to identify titles forcontent production.

FIG. 2 shows a block diagram of a data processing system which can beused in various embodiments of the disclosed system and method.

FIG. 3 shows one embodiment a method for evaluating search queries toidentify titles for content production.

DETAILED DESCRIPTION

The following description and drawings are illustrative and are not tobe construed as limiting. Numerous specific details are described toprovide a thorough understanding. However, in certain instances, wellknown or conventional details are not described in order to avoidobscuring the description. References to one or an embodiment in thepresent disclosure are not necessarily references to the sameembodiment; and, such references mean at least one.

Reference in this specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the disclosure. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment, nor are separate or alternative embodimentsmutually exclusive of other embodiments. Moreover, various features aredescribed which may be exhibited by some embodiments and not by others.Similarly, various requirements are described which may be requirementsfor some embodiments but not other embodiments.

For the purposes of this disclosure, “search term” should be understoodto represent a string of one or more tokens that can convey a concept orset of concepts and can be used to search a database for entriesrelating to such concepts. A search term could comprise a singlecharacter or symbol, a single keyword or keyword stem, or group ofkeywords or a natural language phrase. Search terms can be used to formsearch queries, and search queries comprise one or more search terms.

Overview

A ready, and comprehensive, source of information that can provideup-to-the-minute insight into topics that are of interest to a broadarray of users exists: query logs from, for example, Internet searchservices. Most, if not all, Internet users initially locate informationrelating to topics of interest using search queries. Thus, query logscan include search terms from millions of users that show, in detail,topics for which users are currently seeking more information. Thechallenge, however, presented by such query logs is the sheer volume ofthe information that precludes manual processing or analysis.

Various embodiments of the systems and methods disclosed herein relateto systems and methods for analyzing query logs to identify searchqueries relating to topics that could be used to define titles foronline content having the greatest potential for generating onlinerevenue. In one embodiment, queries are matched to search terms whoserevenue generating potential has been estimated, and queries which are aclose match to search terms having strong revenue generating potentialare selected as titles for online content that can, for example, becreated by an online content provider. In one embodiment, as discussedfurther below, a matching score may be calculated to represent theextent of matching between search terms having strong revenue generatingpotential and search queries.

One measure of the revenue generating potential of a search query is thelifetime value (LTV) of the query. The LTV of a search query is anestimate of the total online revenue content relating to the searchquery that is expected to be generated over its lifetime. In oneembodiment, the LTV of a search query is estimated by matching thesearch query to search terms whose LTV is known. When the term that bestmatches the search query has been identified, the LTV value of that termcan be used as the estimated LTV for the search query.

An LTV estimate of an individual search query based on the LTV of theclosest matching search term can be viewed as a best case estimate forcontent that can be produced with a title based on that search query.Once the content has been produced, many other factors affect the actualLTV of the content. It can be generally assumed that in aggregate, thetotal pieces of content connected to an individual LTV term will averagethat LTV amount.

An Illustrative Implementation

FIG. 1 illustrates a high-level view of components of one embodiment ofsystems that can support evaluating search queries to identify titlesfor content production. A content service provider 120 provides onlinecontent to online publishers 180. Such content could include any form ofelectronic content capable of distribution to end users, such asdocuments and multimedia objects. Such content could relate to any topicof potential interest to online users. Online publishers 180 could offera broad array of content relating to many topics, or could offernarrowly focused subject matter. In one embodiment, online publishers180 pay the content provider for online content using any monetizationtechniques known in the art, such as, for example, by subscription, aflat fee for individual objects or a cost-per-view for individualobjects (e.g. a fee charge every time a user views the object on theonline publisher's website).

In one embodiment, the content service provider 120 selects titles ofpotential interest using various techniques, which could includeidentifying titles having the greatest revenue generating potential.Such titles can then be provided to content authors who develop contentrelating to the titles for the content provider 120. The authors 180could be employees of the content service provider 120 or could beindependent contractors or employees of another entity. In oneembodiment, the content provider 120 identifies potential titles, atleast in part, by analyzing query logs for Internet searches performedby users.

In one embodiment, content title selection servers 122 collect querylogs from web search services 140 over a network 190, such as theInternet. In one embodiment, the web search service provider maintainsone or more web search servers 142 connected to the Internet thatprovide web search services (e.g. web queries) to end users. The websearch servers 142 maintain one or more query logs 144 that log all, orsubstantially all, web queries entered by end users. In one embodiment,the web search servers 140 provides means, such as an API or an FTPserver, for content title selection servers 122 of a content serviceprovider to periodically or continuously download data derived from websearch query logs 144, such as, for example, all queries issued by userswithin a defined time range.

In one embodiment, the content title selection servers 122 process querylog data retrieved from the web search service provider 140 to identifysearch queries that are potential content by estimating the LTV of thesearch queries and selecting search queries having an LTV greater than apreset threshold. In one embodiment, the content service providermaintains one or more content selection databases 144 that include LTVsfor a plurality of search terms (i.e. LTV search terms). In oneembodiment, the LTVs of search queries are estimated by matching thesearch queries to LTV terms in the content selection databases 144using, for example, the techniques similar or identical to thosedescribed below in the section titled “An Illustrative Algorithm toMatch Search Queries to Search Terms.”

The systems shown in FIG. 1 are purely illustrative, and otherconfigurations are possible, as will be readily apparent to thoseskilled in the art. For example, web query services, content developmentand authoring services and online publishing services could all beprovided by a single service provider and hosted on one or a cluster ofnetworked servers located at one or more physical locations.

FIG. 2 shows a block diagram of a data processing system which can beused in various embodiments of the disclosed system and method. WhileFIG. 2 illustrates various components of a computer system, it is notintended to represent any particular architecture or manner ofinterconnecting the components. Other systems that have fewer or morecomponents may also be used.

In FIG. 2, the system 201 includes an inter-connect 202 (e.g., bus andsystem core logic), which interconnects a microprocessor(s) 203 andmemory 208. The microprocessor 203 is coupled to cache memory 204 in theexample of FIG. 2.

The inter-connect 202 interconnects the microprocessor(s) 203 and thememory 208 together and also interconnects them to a display controllerand display device 207 and to peripheral devices such as input/output(I/O) devices 205 through an input/output controller(s) 206. Typical I/Odevices include mice, keyboards, modems, network interfaces, printers,scanners, video cameras and other devices which are well known in theart.

The inter-connect 202 may include one or more buses connected to oneanother through various bridges, controllers and/or adapters. In oneembodiment the I/O controller 206 includes a USB (Universal Serial Bus)adapter for controlling USB peripherals, and/or an IEEE-1394 bus adapterfor controlling IEEE-1394 peripherals.

The memory 208 may include ROM (Read Only Memory), and volatile RAM(Random Access Memory) and non-volatile memory, such as hard drive,flash memory, etc.

Volatile RAM is typically implemented as dynamic RAM (DRAM) whichrequires power continually in order to refresh or maintain the data inthe memory. Non-volatile memory is typically a magnetic hard drive, amagnetic optical drive, or an optical drive (e.g., a DVD RAM), or othertype of memory system which maintains data even after power is removedfrom the system. The non-volatile memory may also be a random accessmemory.

The non-volatile memory can be a local device coupled directly to therest of the components in the data processing system. A non-volatilememory that is remote from the system, such as a network storage devicecoupled to the data processing system through a network interface suchas a modem or Ethernet interface, can also be used.

In one embodiment, the content title selection servers 122, web searchservers 142, as well as systems used by content authors 160 and onlinepublishers 180 of FIG. 1 can be implemented using one or more dataprocessing systems as illustrated in FIG. 1. In some embodiments, one ormore servers of the system illustrated in FIG. 1 can be replaced withthe service of a peer to peer network or a cloud configuration of aplurality of data processing systems, or a network of distributedcomputing systems. The peer to peer network, or cloud based serversystem, can be collectively viewed as a server data processing system.

Embodiments of the disclosure can be implemented via themicroprocessor(s) 203 and/or the memory 208. For example, thefunctionalities described above can be partially implemented viahardware logic in the microprocessor(s) 203 and partially using theinstructions stored in the memory 208. Some embodiments are implementedusing the microprocessor(s) 203 without additional instructions storedin the memory 208. Some embodiments are implemented using theinstructions stored in the memory 208 for execution by one or moregeneral purpose microprocessor(s) 203. Thus, the disclosure is notlimited to a specific configuration of hardware and/or software.

FIG. 3 shows in one embodiment a method for evaluating search queries toidentify titles for content production. In one embodiment, one or moreservers such as the content title selection servers shown in 122 of FIG.1 perform the operations of the method shown in FIG. 3, and contenttitle selection databases such as that shown in 124 of FIG. 2 store thedata collected and/or used by various operations of the method.

In block 320, one or more processes running on a server collect searchquery logs from one or more service providers over a network, such asthe Internet. The service providers could be web search serviceproviders, or could be other types of services that collect and analyzequery logs from web search service provides and other online businesses.The query logs could be collected using any technique known in the artsuch as a real-time feed, an API or batched data transfers via FTP orsome other bulk data transmission protocol. The query logs could beprovided in any form such as, for example, raw text, compressed text ortext encoded using any encoding scheme known in the art. Data could becollected continuously or periodically, such as once per week or onceper month.

In block 330, one or more processes running on the server filter searchquery logs to eliminate or reduce the volume of undesirable or unusabledata in query log data. In one embodiment, the query log data isprocessed to eliminate duplicate queries. In one embodiment, counts forindividual search queries could be determined and queries that do notappear at least a threshold number of times in a given time period, forexample, 100 in a week, can be dropped. In one embodiment, commonlymisspelled words can be corrected. In one embodiment, search termscontaining blacklisted words or phrases, for example, obscene, racist,or sexist terms or phrases, can be dropped. Other filter criteria willbe readily apparent to those skilled in the art. In one embodiment, thisoperation is optional, and the succeeding operations of the methodprocess raw query log data.

In block 340, one or more processes running on the server determine thelifetime value (LTV) of each search query by matching each query tosearch terms whose LTV is known. In one embodiment, search terms withknown LTVs are stored on a database accessible to the processes runningon the server. The LTV of such search terms can be determined by avariety of methods, including those described below in “An IllustrativeMethod to Determine the LTV of Search Terms.” In one embodiment, searchqueries are matched to search terms using matching techniques similar oridentical to those described below in “An Illustrative Algorithm toMatch Search Queries to Search Terms” in which a matching score iscomputed for each search term matched to a search query.

In one embodiment, the LTV of the search query is set equal to the LTVof the search term having the highest matching score to the searchquery. In one embodiment, the LTV of the search query is set equal to anaverage of the LTV of a predetermined number of search terms, forexample, three, having the highest matching score to the search query.In one embodiment, search terms having a matching score to the searchquery below a threshold, for example, 0.2, are deemed not to match. Ifno search terms match the search query, the search query may be deemedto have an LTV of zero or some predetermined default value or may bediscarded.

In block 360, one or more processes running on the server selectpotential titles from search queries whose LTV is known using at leastone selection criteria that relates to the LTV of the respectivepotential titles. In one embodiment, search queries having a minimum LTVare selected. In one embodiment, search queries within a range of LTVsare selected (e.g. 120-240).

In one embodiment, potential titles could be selected using a rule basedprocess such as that described in Provisional Application Ser. No.61/307,702, filed Feb. 24, 2010, entitled “Rule-Based System and Methodto Associate Attributes to Text Strings” the entire contents of whichare incorporated by reference herein in its entirety. In one embodiment,the selection criteria could comprise rules relating to, a contentprovider's publication criteria, such as, for example, including orexcluding specific categories or topics or including or excludingcombinations of specific categories or topics, traffic estimates for thesearch queries, advertising rates, and click-through-rate. Suchprocessing could further associate additional metadata with potentialtitles as disclosed in Provisional Application Ser. No. 61/307,702. Inone embodiment, potential titles, and generated metadata if applicable,could be input to further selection processes, which could includefurther selection or rejection by other automated processes or humaneditors.

In block 370, one or more processes running on the server normalize 370the text of selected titles. In one embodiment, the text of selectedtitles are edited to create clear, comprehensible title text that isgrammatically correct. In one embodiment, if common spelling errors werenot corrected earlier in the process, spelling errors are corrected atthis time. In one embodiment, title text is automatically normalizedusing natural language processing techniques such as those which arewell known to those skilled in the art. In one embodiment, title textmay be normalized by human editors. In one embodiment, this operation isoptional, and potential titles comprise raw search queries selected bythe preceding operations of the methods and, if applicable, associatedmetadata produced in block 360 as described above.

In block 380, one or more processes running on the server provide thenormalized titles to content authors for development of content relatedto the title text. In one embodiment, the normalized titles can beprovided electronically to content authors using any method now known orto be later developed suitable for the delivery of electronicinformation such as, for example, by email, batch FTP or an interactivewebsite. Alternatively, the normalized titles can be provided to contentauthors in non-electronic form such as, for example, hard copy reports.

Alternatively, or additionally, the normalized titles can be stored onone or more databases such as, for example, the content title selectiondatabases such as that shown in 124 of FIG. 2. The databases containingnormalized titles could then be used, for example, to produce reports,emails or be linked to an interactive website accessible to contentauthors.

An Illustrative Method to Determine the LTV of Search Terms

In one embodiment, the LTV of search terms can be determined usingtechniques as disclosed in U.S. patent application Ser. No. 12/337,550,entitled “Method and System for Ranking of Keywords,” the entiredisclosure of which is included by reference herein in its entirety. Inone embodiment, the LTV of a search term can be calculated as

LTV=TE×AR×CTR×12×LV

where

-   -   TE is a traffic estimate (e.g. monthly)    -   AR is an advertising rate    -   CTR is a click-through-rate    -   12 is used to scale the calculation from monthly to yearly    -   LV is a longevity value of the search term in years        (note that LTV is one embodiment of a “ranking value” or RV, as        disclosed in the Ser. No. 12/337,550 application).

In one embodiment, the traffic estimate (TE) is an estimate of thenumber of views per month that content built around a search term ispredicted to receive. In one embodiment, the TE can estimated usingvarious techniques using factors which could include scores indicatingthe bid values of search terms, the click through rates for searchterms, the search volume of search terms, the ranking of websites insearches returned as matches to search terms, the number of words insearch terms, the number of letters in a search term, and thecompetitiveness of the category into which search terms fall.

In one embodiment, the advertising rate (AR) is a measure of how muchadvertisers are paying for each search term. The AR can be determinedfrom information obtained for a fee or for free from other sources, suchas those maintained by companies offering search engines and othersearch services. In one embodiment, data are obtained from variousadvertising rate sources, and are analyzed and combined to produce a netvalue for the AR.

In one embodiment, the click-through-rate (CTR) is a measure of how manypeople clicked on or selected advertisements that appeared in connectionwith search results for a search term. Various methodologies can beemployed to determine click-thru metrics using only empirical datarecording the number of searches relating to a specific term and thefrequency with which those searchers click on advertising. In oneembodiment, the value of the Click-Thru Rate (CTR) can range between0.1% to 40%.

In one embodiment, the longevity value (LV) is a measure of thelongevity associated with content built around a particular search term(e.g. how many years will there be a demand for the content). In oneembodiment, categories of search terms are mapped to durations of timebased on expert analysis. Search terms can then be mapped to a category.For example, in one embodiment, the search term “shovel” falls under thecategory “gardening” and “DVD player” falls under the category “homeelectronics.” The search term can then be assigned the LV of thematching category.

An Illustrative Algorithm to Match Search Queries to Search Terms

In one embodiment, a search query can be matched to search terms withknown LTVs by determining how many words exist in both the search queryand the search terms. The more words the search query and any given termhave in common, the better the match. When the term that best matchesthe search query has been identified, the LTV value of that term becomesthe estimated LTV for the search query.

In one embodiment, a matching score between an LTV term (a term having aknown LTV) and a search query that is a potential title can be expressedas a number between 0 (no match) and 1 (perfect match). It depends on:

-   -   (i) The weighted count of words in the LTV term, W_(ltv)    -   (ii) The weighted count of the potential title (search query)        words, W_(t)    -   (iii) The weighted count of unique matching words between the        LTV Term and the Title, W_(m)

In one embodiment, the count for each word in the search query and theLTV search term assigned a weight reflecting the relative significanceof the word for matching purposes. In one embodiment, the words areweighted in such a way that stop words receive 10% of the weight of anon-stop word. The weighting for stop words and non-stop words is notlimited to the one described. Other weighting factors that could betaken into account are:

-   -   The part of speech of a word.    -   The length of a word.    -   The corpus frequency of a word.    -   Feedback from a human.

Thus, in one embodiment, words which are deemed as relativelyunimportant in understanding the meaning of a search query or searchterm are assigned a weight of 0.1. In one embodiment, such words couldinclude categories of words, for example stop words, prepositions andconjunctions. Such words could also include specific, commonly usedwords that generally do not convey additional information, such as“use.” In one embodiment, a list of words that are assigned a weight of0.1 can be stored on a database.

In one embodiment, words which are deemed as relatively important inunderstanding the meaning of a search query or search term are assigneda weight of 1.0. In one embodiment, such words could include categoriesof words, for example nouns. Such words could also include specificwords generally convey additional information, such as “easiest.” In oneembodiment, a list of words that are assigned a weight of 1.0 can bestored on a database.

In one embodiment, weights could be assigned to words based on theirusage within a search query or LTV term using natural languageprocessing, heuristic rule based processing or classifiers trained usingmanually labeled data. In the example below, “a,” “to” and “use” areassigned a weight of 0.1 and the remaining words are assigned a weightof 1.0.

In one embodiment, the words are matched verbatim, ignoring order,punctuation and casing. Even a difference in one character (“budget” vs.“budgets”) will result in a no-match. The rationale behind this is thatmany LTV Terms have minor differences, but can have significantlydifferent LTVs. When calculating W_(m), matching words are only countedonce, even if they occur multiple times in the potential title and/orthe LTV term.

In one embodiment, the matching score function can be expressed as:

MatchingScore=(W _(m) ÷W _(ltv))²×(W _(m) ÷W _(t))

-   -   where (W_(m)÷W_(ltv))² can be interpreted as the percentage        (squared) of the LTV term that is matching;    -   and (W_(m)÷W_(t)) can be interpreted as the percentage of the        search query (potential title) that is matching.

Examples of the application of the above algorithm are as follows.Suppose a search query extracted from a search query log is:

“Easiest Hearing Aid to Use”

and three LTV search terms that at are a partial match to the searchquery (e.g. includes at least one word from the search query) areselected from a database of LTV search terms comprising:

1. hearing aid

2. a hearing aid

3. hearing

In actual practice, for any given search query, the number of searchterms that are at least a partial match to a given search query couldvary greatly, from zero to hundreds of possible matches. In oneembodiment, a search query could be matched against every entry in a LTVsearch term database. In one embodiment, search terms comprising atleast one word from the search query could be selected usingconventional database query facilities, such as via SQL queries. Inanother embodiment, search terms comprising at least one word from thesearch query could be selected from in an in-memory list of searchterms.

In one embodiment, matching scores for the example data can be computedper the above methodology as follows. Note in the following matchingscore computations, the matching words are underlined and the weightedword score computation is in brackets.

1. Hearing Aid

Easiest Hearing Aid to Use<=>hearing aid

[1+1+1+0.1+0.1] [1+1]

W_(t)=3.2

W_(ltv)=2

W_(m)=2

The matching score is:

(2÷2)²×(2÷3.2)=0.6250

2. A Hearing Aid

Easiest Hearing Aid to Use<=>a hearing aid

[1+1+1+0.1+0.1] [0.1+1+1]

W_(t)=3.2

W_(ltv)=2.1

W_(m)=2

The matching score is:

(2÷2.1)²×(2÷3.2)=0.5669

3. Hearing

Easiest Hearing Aid to Use<=>hearing

[1+1+1+0.1+0.1] [1]

W_(t)=3.2

W_(ltv)=1

W_(m)=1

The matching score is:

(1÷1)²×(1÷3.2)=0.3125

Thus, in the above example, the LTV term “hearing aid” has the highestmatching score, and in one embodiment, the LTV of this term could beused to set the estimated LTV of the search query “Easiest Hearing Aidto Use.” As an example, if the estimated LTV of the search query equalsor exceeds a predetermined monetary threshold, then the search query canbe tagged as being suitable for use as a title in a content item. Thesearch query/title may be sent, for example, to a content author for thecreation of an content item having this title.

In one embodiment, in order to avoid false positives, (i.e. incorrectmatches), a minimum matching score can be set that that must be met orexceeded. A score below that threshold is treated as a no-match.Experiments have determined that 0.2 is a good threshold for the type oftitles and LTV terms most commonly evaluated. For example, assume thereis an LTV search term “to use financial aid” which shares three words incommon with the search query “Easiest Hearing Aid to Use.” The matchingscore for this term can be computed.

Easiest Hearing Aid to Use<=>to use financial aid

[1+1+1+0.1+0.1] [0.1+0.1+1+1]

W_(t)=3.2

W_(ltv)=2.2

W_(m)=1.2

The matching score is:

(1.2÷2.2)²×(1.2÷3.2)=0.1116

Thus, the term falls below the 0.2 threshold, and this term can beexcluded from further consideration.

CONCLUSION

While some embodiments can be implemented in fully functioning computersand computer systems, various embodiments are capable of beingdistributed as a computing product in a variety of forms and are capableof being applied regardless of the particular type of machine orcomputer-readable media used to actually effect the distribution.

At least some aspects disclosed can be embodied, at least in part, insoftware. That is, the techniques may be carried out in a computersystem or other data processing system in response to its processor,such as a microprocessor, executing sequences of instructions containedin a memory, such as ROM, volatile RAM, non-volatile memory, cache or aremote storage device.

Routines executed to implement the embodiments may be implemented aspart of an operating system, middleware, service delivery platform, SDK(Software Development Kit) component, web services, or other specificapplication, component, program, object, module or sequence ofinstructions referred to as “computer programs.” Invocation interfacesto these routines can be exposed to a software development community asan API (Application Programming Interface). The computer programstypically comprise one or more instructions set at various times invarious memory and storage devices in a computer, and that, when readand executed by one or more processors in a computer, cause the computerto perform operations necessary to execute elements involving thevarious aspects.

A machine readable medium can be used to store software and data whichwhen executed by a data processing system causes the system to performvarious methods. The executable software and data may be stored invarious places including for example ROM, volatile RAM, non-volatilememory and/or cache. Portions of this software and/or data may be storedin any one of these storage devices. Further, the data and instructionscan be obtained from centralized servers or peer to peer networks.Different portions of the data and instructions can be obtained fromdifferent centralized servers and/or peer to peer networks at differenttimes and in different communication sessions or in a same communicationsession. The data and instructions can be obtained in entirety prior tothe execution of the applications. Alternatively, portions of the dataand instructions can be obtained dynamically, just in time, when neededfor execution. Thus, it is not required that the data and instructionsbe on a machine readable medium in entirety at a particular instance oftime.

Examples of computer-readable media include but are not limited torecordable and non-recordable type media such as volatile andnon-volatile memory devices, read only memory (ROM), random accessmemory (RAM), flash memory devices, floppy and other removable disks,magnetic disk storage media, optical storage media (e.g., Compact DiskRead-Only Memory (CD ROMS), Digital Versatile Disks (DVDs), etc.), amongothers.

In general, a machine readable medium includes any mechanism thatprovides (e.g., stores) information in a form accessible by a machine(e.g., a computer, network device, personal digital assistant,manufacturing tool, any device with a set of one or more processors,etc.).

In various embodiments, hardwired circuitry may be used in combinationwith software instructions to implement the techniques. Thus, thetechniques are neither limited to any specific combination of hardwarecircuitry and software nor to any particular source for the instructionsexecuted by the data processing system.

Although some of the drawings illustrate a number of operations in aparticular order, operations which are not order dependent may bereordered and other operations may be combined or broken out. While somereordering or other groupings are specifically mentioned, others will beapparent to those of ordinary skill in the art and so do not present anexhaustive list of alternatives. Moreover, it should be recognized thatthe stages could be implemented in hardware, firmware, software or anycombination thereof.

In the foregoing specification, the disclosure has been described withreference to specific exemplary embodiments thereof. It will be evidentthat various modifications may be made thereto without departing fromthe broader spirit and scope as set forth in the following claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative sense rather than a restrictive sense.

1. A method comprising: collecting, over a network, a plurality ofsearch queries from at least one search query data source; determining,using a computing device, a lifetime value for each of the plurality ofsearch queries to provide a plurality of lifetime values; selecting,using the computing device, a plurality of potential titles from theplurality of search queries using at least one selection criteriaassociated with the plurality of lifetime values; providing theplurality of potential titles to at least one content author.
 2. Themethod of claim 1 wherein the lifetime value is determined for each ofthe plurality of search queries by matching each respective search queryto at least one search term having a known lifetime value.
 3. The methodof claim 2 wherein each of the plurality of search queries are matchedto each of the at least one search term by computing a matching scorefor the respective search term using the respective search term and therespective search query.
 4. The method of claim 3 wherein the matchingscore is computed according to the equation:MatchingScore=(W _(m) ÷W _(ltv))²×(W _(m) ÷W _(t)), where W_(ltv), is aweighted sum of words in the respective search term, W_(t) is a weightedcount of words in the respective search query, W_(m) is a weighted countof unique matching words between the respective search term and therespective search query.
 5. The method of claim 3 wherein the lifetimevalue for each of the plurality of search queries is set equal to thelifetime value of one of the at least one search terms having thehighest matching score to the respective search query.
 6. The method ofclaim 3 wherein the lifetime value for each of the plurality of searchqueries is set equal to an average of the lifetime value of a set of theat least one search terms having the highest matching scores to therespective search query.
 7. The method of claim 3 wherein if one or moreof the plurality of search queries do not match any of the plurality ofsearch terms, the lifetime value of the respective search query is setto a default value.
 8. The method of claim 1 wherein the at least oneselection criteria comprises a minimum search query LTV.
 9. The methodof claim 1 wherein the at least one selection criteria comprises a setof rules.
 10. The method of claim 1 wherein the using the at least oneselection criteria comprises the generation of metadata for each of theplurality of search queries.
 11. The method of claim 1 additionallycomprising: filtering, using the computing device, the plurality ofsearch queries.
 12. The method of claim 11 wherein filtering compriseseliminating duplicate search queries from the plurality of searchqueries.
 13. The method of claim 11 wherein filtering compriseseliminating search queries that do not appear in the plurality of searchqueries at least a threshold number of times.
 14. The method of claim 11wherein filtering comprises eliminating search queries having one ormore words on a blacklist from the plurality of search queries.
 15. Themethod of claim 11 wherein filtering comprises correcting misspelledwords in the plurality of search queries.
 16. The method of claim 1additionally comprising: normalizing, using the computing device, eachof the plurality of potential titles.
 17. The method of claim 16 whereinnormalizing comprises correcting spelling errors in each of theplurality of potential titles.
 18. The method of claim 16 whereinnormalizing comprises correcting grammatical errors in each of theplurality of potential titles.
 19. A machine readable media embodyinginstructions, the instructions causing a data processing system toperform a method, the method comprising: collecting, over a network, aplurality of search queries from at least one search query data source;determining, using a computing device, a lifetime value for each of theplurality of search queries to provide a plurality of lifetime values,wherein the lifetime value is determined for each of the plurality ofsearch queries by matching each respective search query to at least onesearch term having a known lifetime value by computing a matching scorefor the respective search term using the respective search term and therespective search query; selecting, using the computing device, aplurality of potential titles from the plurality of search queries usingat least one selection criteria associated with the plurality oflifetime values; adding, using the computing device, the plurality ofpotential titles to a database stored on a computer readable medium. 20.A computer system comprising: a memory; and at least one processorcoupled to the memory to: collect a plurality of search queries from atleast one search query data source; determine a lifetime value for eachof the plurality of search queries to provide a plurality of lifetimevalues, wherein the lifetime value is determined for each of theplurality of search queries by matching each respective search query toat least one search term having a known lifetime value by computing amatching score for the respective search term using the respectivesearch term and the respective search query; select a plurality ofpotential titles from the plurality of search queries using at least oneselection criteria associated with the plurality of lifetime values; addthe plurality of potential titles to a database stored on a computerreadable medium.